Ckmeans.1d.dp: Optimal and Fast Univariate Clustering

Fast optimal univariate clustering and segementation
by dynamic programming. Three types of problem including
univariate k-means, k-median, and k-segments are solved with
guaranteed optimality and reproducibility. The core algorithm
minimizes the sum of within-cluster distances using respective
metrics. Its advantage over heuristic clustering algorithms in
efficiency and accuracy is increasingly pronounced as the
number of clusters k increases. Weighted k-means and unweighted
k-segments algorithms can also optimally segment time series
and perform peak calling. An auxiliary function generates
histograms that are adaptive to patterns in data. In contrast to
heuristic methods, this package provides a powerful set of tools
for univariate data analysis with guaranteed optimality.
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